Reliability assessment using constant-stress accelerated degradation data based on pseudo life distribution

A method based on pseudo life distribution is proposed to evaluate reliability and predict lifetime using accelerated degradation data. Firstly, according to the characteristics of accelerated degradation data encountered in engineering, the analysis method for time continuous performance degradation series is given, and then degradation models for the whole samples are established. Secondly, a reliability function is derived based on the accelerated model, where the unknown parameters of the reliability function are obtained by integral inference with maximum likelihood estimation. The reliability confidence interval is given via Monte Carlo simulation using a Fisher information matrix. With the effective usage of degradation data under different accelerated stress levels as well as the strong adjustment advantages of the time continuous model, the accuracy reliability assessment and lifetime prediction of products are improved and a novel technical way to evaluate of reliability and predict lifetime based on accelerated degradation data is provided. Finally, an example is presented which can illustrate the performance of the proposed method.